137 results about "Context model" patented technology

The invention relates to the field of intelligent interaction, and discloses an intelligent hardware dynamic interaction system based on voice semantic fusion and multi-modal perception, which comprises the following steps of: constructing a context model of continuous operation by collecting continuous voice instructions, gesture actions and expression information of a user; semantic analysis and feature fusion are carried out on currently collected voice, gesture and expression features, meanwhile, credibility indexes of all modes are calculated through a weighting or deep learning model, weighting correction is carried out on a fusion result, a real-time feedback algorithm is adopted for weight adjustment for continuous optimization, the next operation intention of a user is predicted through deep learning, and the user experience is improved. And in combination with historical interaction data, online feedback and prediction errors, context management, modal weight and intention prediction strategies are adaptively optimized, and the updated strategies are used for next-round context acquisition and multi-modal fusion. The method has the advantage of improving the recognition accuracy in the continuous interaction scene.

A data platform monitors a compute environment by performing multi-stage heuristic analysis of event data representing a plurality of events occurring within the environment. The platform utilizes multiple event analyzers, each configured according to a distinct analysis heuristic, to evaluate different subsets of the event data and generate corresponding output signals. A higher-level event analyzer applies a further heuristic to the multiple output signals to generate a composite alert signal, indicating whether the combination of analyzed events collectively represents a security intrusion or other anomalous condition of sufficient severity to warrant alerting. Based on the composite alert signal, the platform performs an alert-based operation, such as generating a user-facing alert, initiating an automated mitigation, or updating a contextual model of system behavior. By combining the analytical outputs of heterogeneous heuristics, the disclosed architecture enhances the accuracy and contextual relevance of automated intrusion detection within complex computing environments.

The invention discloses an enterprise data analysis system based on natural language interaction, and relates to the field of enterprise data analysis, the system comprises seven core function modules: a dialogue management module supports a user to create a new dialogue and automatically jumps to an initial page; the report interaction module realizes query support and switching functions of a real-time report and a T + 1 report through a floating button; the file import module allows uploading of Word, PDF and JPG format files to supplement question and answer contexts; the model configuration module supports dynamic switching of different AI service interfaces; the interaction auxiliary module provides a quick question button and a dialogue interruption function; the report preview module integrates Excel and PDF plug-ins to realize detail data preview; the session sharing module generates a sharing link and sets an access permission rule; dynamic model configuration optimizes response speed and accuracy; the operation process is simplified by a suspension button and a quick questioning function; the session sharing mechanism supports cross-team cooperation; and the real-time collaborative design with the T + 1 report meets the multi-dimensional data analysis requirement.

The invention provides an edge multi-mode grading detection elder falling early warning method comprising the following steps: deploying an edge computing network comprising a master control node and a data node, and loading a context model according to a position; binding the face information of the user with the RFID tag through an identity confirmation camera; the data node triggers a millimeter wave radar to collect point cloud data through a human body infrared sensor; performing contour change rate primary detection, context model deep learning secondary detection and voice module state confirmation tertiary response; and after the tumble is confirmed, the master control node gives an early warning to the emergency center. The invention relates to the technical field of elderly monitoring.

The invention discloses a short video promotion optimization method and system, relates to the technical field of short video promotion optimization, and aims to solve the problems that when an existing short video promotion method is used for processing subtle elements which may cause different understandings of users in video contents, intentions of merchants are difficult to accurately convey, target users are difficult to reach, and the user experience is poor. The method comprises the steps of obtaining content data of a to-be-promoted video, performing multi-meaning element identification on the content data to determine at least one potential multi-meaning element in the to-be-promoted video, and determining a situational misreading risk assessment result of the potential multi-meaning element according to the potential multi-meaning element and a preset user situation model, and according to the situational misreading risk assessment result, a pre-stored promotion constraint condition and a content modification cost model, making a decision between the content modification strategy and the audience screening strategy to generate an initial intervention strategy, executing the initial intervention strategy, and optimizing the promotion process based on user feedback data obtained after execution.

The embodiment of the invention provides an end-to-end image compression method and system based on window local attention and generalized chessboard space channel context, and belongs to the technical field of image processing. The method comprises the following steps: constructing a transformation network based on an attention module and a stacked residual block; the transformation network based on the attention module and the stacked residual block is used for executing adaptive transformation of contents through dynamic representation and neighborhood information embedding to obtain potential features; establishing a generalized chessboard space channel context model; the generalized chessboard space channel context model is used for carrying out entropy coding on the potential features; and obtaining image compression data according to the transformation network based on the attention module and the stacked residual block and the generalized chessboard space channel context model. According to the method, redundancy can be eliminated to the maximum extent, excellent rate distortion performance is achieved, and meanwhile high-throughput parallel computing efficiency is ensured.

A method of video encoding includes performing context modeling to determine a context model for each of a number of bins of syntax elements corresponding to residues of a region of a transform skipped block in a current picture. The number of the bins of syntax elements being context coded does not exceed a maximum number of context coded bins set for the region. The method further includes encoding, according to Block Differential Pulse Code Modulation (BDPCM), the syntax elements based on the determined context models. When the maximum number of context coded bins is reached, remaining bins of syntax elements are encoded based on a bypass model.

The invention discloses a BIM-based construction resource real-time management method and system, and relates to the technical field of building construction. The method comprises the steps of obtaining real-time Internet of Things data flow and BIM model data of a construction site, dynamically associating physical resources in the Internet of Things data flow to a task component of a BIM model, and generating contextual consistency of fused sensing data and a current task; constructing a dynamic context model; inputting the current state of the dynamic context model into a preset construction knowledge graph, identifying an abnormal event, performing causal path tracing on the abnormal event, and outputting an abnormality diagnosis report at least including diagnosis confidence; and pushing the abnormality diagnosis report to a manager, receiving feedback data, and optimizing the inference rule of the construction knowledge graph by using the feedback data and the abnormality diagnosis report. According to the invention, the accuracy and adaptability of intelligent decision-making of construction resource management are effectively improved.

The invention discloses a design method for an on-orbit accompanying measurement and control communication machine state machine model, and the method comprises the steps: constructing a data model of a measurement and control communication machine state machine, and enabling the data model to be used for calling data externally; constructing a context model of the state machine of the measurement and control communication machine based on the data model, and constructing a logic architecture of the state machine of the measurement and control communication machine based on scene service data; based on the logic architecture, the context model obtains an executable extended state machine and updates the feature attributes; obtaining a simulation model through periodic continuation based on the state machine capable of executing expansion and the context model, wherein the simulation model is used for processing uplink and downlink remote control and telemetering, voice and distance measurement service data of satellite communication, responding to a communication machine instruction set of the state machine of the measurement and control communication machine and outputting communication machine product telemetering; universal configuration is implemented based on the communicator instruction set and the communicator product telemetry.

The invention relates to the technical field of automatic driving, in particular to an open type automatic driving safety management system and method supporting multi-benefit-party strategy arbitration, and the system comprises an open type safety strategy interface module which is used for defining a standardized strategy description specification; the strategy plug-in management module is used for receiving and loading the strategy plug-ins conforming to the strategy description specifications; the perception and fusion module is used for collecting the vehicle state data, the environment perception data and the service context data and fusing the data to generate a unified context model; the multi-strategy evaluation module is used for screening a candidate strategy set according to a unified context model and a trigger condition when an abnormal event is detected, and evaluating and selecting an optimal strategy; and the strategy execution and monitoring module is used for executing the action template corresponding to the optimal strategy. According to the invention, the automatic driving vehicle can select adaptive degradation strategies according to different operation scenes and business requirements, and the adaptability of the system to diversified business scenes is improved.

The embodiment of the application provides a kind of quantization parameter coding method and electronic equipment, the coding method includes: first, according to the QP reconstructed value of the QP of coded non-private CU in CU QP group, determine the QP prediction value of non-private CU to be coded in CU QP group;Then, according to the QP original value of non-private CU to be coded and the QP prediction value of non-private CU to be coded, determine the QP residual of non-private CU to be coded;Subsequently, according to the number of coded non-private CU in CU QP group, determine the context model corresponding to the QP residual of non-private CU to be coded;After that, according to the context model corresponding to the QP residual of non-private CU to be coded, the QP residual of non-private CU to be coded is entropy coded. In this way, it can be guaranteed that the decoding process of the QP of non-private CU at the decoding end and the encoding process of the QP of non-private CU at the encoding end are consistent.

An artificial intelligence (AI) assisted generative digital task fulfillment process within digital model platforms is provided. Disclosed are methods and systems for carrying out digital tasks through generative AI, including tasks related to the streamlined design, validation, verification, certification, assembly, operations, and maintenance processes of complex systems. The method includes receiving access to a context AI model trained on Internet-scale data, receiving a user prompt indicating the digital task, and generating contextual data based on the user prompt using the context AI model, where the contextual data identifies a syntax AI model. The method includes training the syntax AI model to generate a template script having a placeholder variable for a parameter related to the digital task. The method also includes using a parameter substitution process to generate the orchestration script by substituting the variable with a parameter value.

The context-aware optimization method includes training a context model by determining whether to split each node in the context by identifying a first subset of virtual context to evaluate by identifying a second subset of virtual contexts to evaluate and obtaining an encoding cost of splitting of the context model for each virtual context in the second subset and identifying the first subset of virtual contexts to evaluate by selecting a predetermined number of virtual contexts from the second subset based on the encoding cost such that the predetermined number of virtual contexts with lowest encoding cost are selected. The modified tree-traversal method includes encoding a mask or performing a speculative-based method. The modified entropy coding method includes representing data into an array of bits, using multiple coders to process each bit in the array and combining the output from the multiple coders into a data range.

An image decoding method performed by a decoding device according to the present document comprises the steps of: deriving a context model for a sign flag of a current residual coefficient of a current block; and decoding the sign flag on the basis of the context model, wherein the context model for the sign flag is derived on the basis of a sign flag of a residual coefficient decoded before the current residual coefficient in the current block.

A computer-implemented system and method for building context models in real time is provided. A contextual situation of a user is determined and compared with models each associated with actions that represent a situation. A determination is made that none of the models represents the situation of the user. A similarity value is determined for each of at least a portion of the models with the situation of the user. A threshold is applied to the similarity values. All models associated with a similarity value that satisfies the threshold are selected. The selected models are merged into a new model for the situation by utilizing a weight associated with each of the selected models to identify those actions in the selected models for inclusion in the new model.

The present invention relates to an image encoding / decoding method and device. The image decoding method according to an embodiment of the present invention may comprise the steps of: acquiring a bitstream including an encoded syntax element; determining a context model for the encoded syntax element; determining a probability model on the basis of the context model; decoding the encoded syntax element on the basis of the probability model; and determining whether to update the probability model.

In a dialogue system outputting utterances in a dialogue manner, an utterance filtering device for preventing output of possibly problematic expression includes: a pre-trained context model trained, in response to an input of a word vector sequence representing an utterance, to output a probability vector comprising elements indicating probability of each of the words in a prescribed word group appearing in a context in which the utterance is placed; and a determining unit 456 configured to input a word vector sequence representing a subject utterance to the context model, and to determine whether the subject utterance is to be discarded or approved depending on whether a value determined as a prescribed function of a probability vector output by the context model in response to the input is equal to or larger than a threshold value.

A video decoding method includes obtaining a to-be-entropy-decoded syntax element in a current block by parsing a received bitstream, where the to-be-entropy-decoded syntax element includes a syntax element 1 or a syntax element 2 in the current block, obtaining a context model corresponding to the to-be-entropy-decoded syntax element, where both of a context model corresponding to the syntax element 1 and a context model corresponding to the syntax element 2 are determined from the same preset context model set, entropy decoding the to-be-entropy-decoded syntax element based on the context model corresponding to the to-be-entropy-decoded syntax element, and obtaining a reconstructed image of the current block based on the syntax element obtained by entropy decoding.

A coding method, a coder, a bitstream and a storage medium are provided. The decoding method comprises: parsing a bitstream, determining first information, the first information being used for indicating a position of a last non-zero coefficient of a current transform block; if the first information indicates that the last non-zero coefficient is located at a top-left corner of the current transform block, decoding a first parameter of the current transform block based on a first context model; wherein the first context model is different from a second context model, and the second context model is a context model used by a transform block decoded before the current transform block.

The invention relates to an elevator target floor display method and system. The elevator target floor display method comprises the steps that building management system data, conference room reservation system data, property management system data and elevator control system data are obtained; converting the collected data into a time situation vector, a space situation vector, an environment situation vector and an event situation vector; retrieving a reference probability list from a pre-constructed reference situation model-floor probability matrix according to the time situation vector and the space situation vector; performing dynamic weighting on the reference probability list based on the environment situation vector and the event situation vector to generate a target layer list sorted according to probability; and dynamically generating an adaptive display interface according to the probability distribution characteristics of the target layer list. According to the scheme provided by the invention, acquisition and processing of privacy data of passengers can be avoided, the problems of authority stiffness and incapability of flexible travel are solved, and the elevator operation efficiency is improved.

Some aspects of the present disclosure relate to systems and methods for performing automated tasks using a robotic system in a human-centric environment. According to a first aspect of the present disclosure, hierarchical object identification is used to generate a contextual model of the environment around the robotic system. According to a second aspect of the present disclosure, a semantic understanding of a task is determined in response to a user query. According to a third aspect of the present disclosure, a task-specific controller is used in combination with a general locomotion controller to execute task or sub-task specific processes in connection with completing a query. One or more aspects of the present disclosure may be used in combination with each other and / or may be used with additional systems and processes for performing automated tasks in a human-centric environment.

To provide a method and apparatus for video encoding or decoding.SOLUTION: When the method uses dependent scalar quantization, the selection of the quantizer depends on the decoding of the preceding transform coefficient, and the entropy decoding of the transform coefficient depends on the selection of the quantizer. In order to maintain high throughput in a hardware implementation of transform coefficient entropy coding, several decision schemes for scalar quantizers are proposed, where the selection of state transitions and context models is based only on the regular coded bins. For example, the state transition is based on the sum of the SIG, gt1 and gt2 flags, the exclusive-OR functions of the SIG, gt1 and gt2 flags, or only the gt1 or gt2 flags. When a block of transform coefficients is coded, the regular mode bins are coded first in one or more scan passes, and the remaining bypass-coded bins are grouped in another one or more scan passes.SELECTED DRAWING: Figure 11

Disclosed herein is an image decoding method. The method of decoding an image comprises obtaining prediction information and residual signal information for a current block from a bitstream and reconstructing, based on the obtained prediction information and the residual signal information, the current block, wherein the prediction information is obtained through entropy decoding, and wherein a context model used for the entropy decoding of the prediction information is determined based on a prediction mode of a neighboring block of the current block.

Provided are a coefficient coding / decoding method, an encoder and a decoder. The method includes: parsing a bitstream by adaptive binary arithmetic coding using a first context model based on quantization coefficient positions to be decoded to obtain a value of a non-zero identification; if the value is a first value, parsing the bitstream by adaptive binary aritlunetic coding using a second context model to obtain values of X preset identifications; if the value of the X-th preset identification is the first value, determining a target order of exponential Golomb coding, and parsing the bitstream using an exponential Golomb decoding algorithm of the target order to obtain remaining values of reconstructed quantization coefficient absolute values; and determining reconstructed quantization coefficient absolute values corresponding to the quantization coefficient positions based on the value of non-zero identification, values of X preset identifications, and remaining values of reconstructed quantization coefficient absolute values.

Various embodiments are directed to apparatuses, methods, computer-readable media, computer program products, and systems related to detecting a process trigger that identifies a target entity; identifying, entity data associated with the target entity; generating, using a machine learning based prediction model, a predictive asset output based at least in part on the entity data, wherein the predictive asset output comprises at least one predicted asset; generating, using a dynamic contextualization model, a multifactor contextualized asset representation for the at least one predicted asset based at least in part on the at least one predicted asset and the entity data; and transmitting the multifactor contextualized asset representation to one or more computing devices via one or more communication channels.

Methods of entropy coding of block partitioning with initial context model probability values indicated by high-level syntaxes or with different context models for HBT (Horizontal Binary Tree) and VBT (Vertical Binary Tree). For one method, one or more coded bits, including encoded data for information related to a partitioning tree of the current picture area, are signalled or parsed from a bitstream. Entropy coding is applied to the coded bits using context formation, including one or more initial context model probability values derived according to a video content type of the current picture, to recover the partitioning tree information. For another method, entropy coding is applied to the coded bits by using one or more context models to recover the first information related to the partitioning tree and the context models are different for HBT and VBT applied to a non-square block.

A method for determining a threshold for entropy coding that skips potential features in image and video coding using a neural network is described. A threshold for estimating a mean of standard deviations based on all latent variables is proposed. For an autoregressive neural network, in order to avoid drifting between context model parameter estimations calculated during training and reasoning, two separate entropy parameter estimation networks, namely an initial entropy estimation network and a refined entropy estimation network, are adopted, the initial entropy estimation network is used for entropy skipping coding and decoding of quantized latent variables, and the refined entropy estimation network is used for entropy skipping coding and decoding of quantized latent variables. A refined entropy estimation network is used for arithmetic encoding and decoding of quantized latent variables. Methods and systems for multi-stage entropy skipping are also presented.

The embodiment of the invention provides a quantization parameter encoding and decoding method and electronic equipment, and the encoding method comprises the steps: firstly, determining a QP predicted value of a to-be-encoded non-privacy CU in a coding unit quantization parameter CU QP group according to a QP reconstruction value of an encoded non-privacy CU in the CU QP group; then, according to the QP original value of the non-privacy CU to be coded and the QP predicted value of the non-privacy CU to be coded, determining a QP residual error of the non-privacy CU to be coded; then, according to the number of the coded non-privacy CUs in the CU QP group, determining a context model corresponding to the QP residual error of the non-privacy CUs to be coded; and then, according to the context model corresponding to the QP residual error of the to-be-coded non-privacy CU, entropy coding is performed on the QP residual error of the to-be-coded non-privacy CU. In this way, it can be ensured that the decoding process of the decoding end for the QP of the non-privacy CU is consistent with the encoding process of the encoding end for the QP of the non-privacy CU.

The invention discloses an English natural language processing method and device based on man-machine interaction and a medium, and relates to the technical field of man-machine interaction intelligent processing, and the method comprises the steps: obtaining an edge weight matrix through a bidirectional long-short-term memory neural network based on a grammar mark sequence, and generating a tense mark sequence through injecting a tense label; constructing a grammatical relation tree according to the tense mark sequence, and outputting a standard grammatical structure by positioning conflict nodes of a main-called structure in real time; constructing a space-time context model, generating a processing channel identifier by dynamically switching a processing channel, and outputting a semantic data packet in combination with a standard grammar structure; and extracting a processing mode mark in the semantic data packet, converting the tense mark sequence into a natural text, and generating a multi-mode terminal response report in combination with the grammar mark sequence and a standard grammar structure. According to the method, a subject-called correlation path analysis mechanism is reconstructed through a distance sensitive radiation algorithm, and the identification bottleneck of traditional dependency analysis on a long-distance separation structure is broken through.

The present application relates to the technical field of medical equipment, and particularly relates to a medical equipment maintenance management method and system. The present application acquires multi-dimensional data such as clinical use characteristics, environmental stress fluctuation and power quality influence of the equipment by constructing a device space-time context model, improves real-time sensing capability of the medical equipment state, establishes a deterioration deduction digital mirror through a standardized stress event sequence and a preset component failure mechanism rule, realizes dynamic monitoring of a real-time cumulative damage index of a core component, overcomes limitations brought by maintenance based on a fixed cycle, compares the real-time cumulative damage index with a failure critical damage interval, can generate a predictive maintenance work order before the equipment really approaches failure, realizes early warning of potential failure, and effectively reduces sudden failure risk, and through positioning of shared causes and analysis of abnormal stress increment, the prevention capability against failure propagation among related equipment is improved.